Administrators of a large video surveillance system can have many thousands of cameras that are continuously recording. While much of this video is never viewed, some of it is very important to the administrator since it can hold evidence of an important event. When an event does occur, the administrator expects the video to be available from the camera(s) that observe the location of the event, in terms of field of view (FOV).
A problem that can occur is that the camera may not catch the intended event for a variety of camera malfunction conditions, either due to external or internal conditions. Externally caused camera malfunction conditions include: the camera may have been knocked from its original position and is no longer recording video of the field of view of the event; or the camera's field of view may have been obstructed, either intentionally through tampering (e.g. camera lens has been spray painted or covered up) or by accident (e.g. an object has been placed in front of the camera, blocking its view). Internally caused camera malfunction conditions result in a degraded image that is not useful evidence of the event, and can include: malfunctioning to the point where no signal is available (sync loss); focus being improperly adjusted, resulting in lack of details; camera iris setting being improperly adjusted, resulting in a white washed image; camera color being inaccurate, resulting in wrongly colored scene; camera signal being very noisy (e.g. due to 60 Hz noise or bad grounding of camera), resulting in loss of information from the scene due to noise; and camera signal level being too low, resulting in an image with lack of contrast.
For administrators of small video surveillance systems, it is possible to manually verify camera operation to determine whether a camera is malfunctioning. An administrator can periodically (e.g. on a weekly basis) verify each camera in the system to ensure that it is properly positioned and operating properly. For administrators of a large network of digital video recorders, this method is usually not feasible since it is too time consuming. The administrator generally assumes that all cameras are operating properly, and only learns of issues when it is too late, e.g. an event occurred and the video was not available. While care is taken when cameras are installed to ensure that they are pointing at the proper area of interest and operating properly, this is not often verified after installation. This is due to the often very large number of cameras, and the time it would take to periodically verify their proper operation manually.
Automated solutions to try to address this problem have been suggested, but they are generally specific to detecting a given camera problem. For example, an automated method exists to detect a camera lens that has been spray painted, or to perform “black screen” detection for obstruction detection, but such methods do not detect any other types of failures or malfunctions. Another known approach can detect changes in the field of view of a camera with respect to more than one measured parameter, but only does so with respect to a single set of reference parameters.
It is, therefore, desirable to provide an improved system and/or method to monitor the operation of cameras in a video surveillance system to detect a camera malfunction.